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2025-12-17 19:47:14 +08:00
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.gitignore vendored Normal file
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target/
!.mvn/wrapper/maven-wrapper.jar
!**/src/main/**/target/
!**/src/test/**/target/
### IntelliJ IDEA ###
.idea/modules.xml
.idea/jarRepositories.xml
.idea/compiler.xml
.idea/libraries/
*.iws
*.iml
*.ipr
### Eclipse ###
.apt_generated
.classpath
.factorypath
.project
.settings
.springBeans
.sts4-cache
### NetBeans ###
/nbproject/private/
/nbbuild/
/dist/
/nbdist/
/.nb-gradle/
build/
!**/src/main/**/build/
!**/src/test/**/build/
### VS Code ###
.vscode/
### Mac OS ###
.DS_Store

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# 默认忽略的文件
/shelf/
/workspace.xml
# 基于编辑器的 HTTP 客户端请求
/httpRequests/
# 依赖于环境的 Maven 主目录路径
/mavenHomeManager.xml
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="DataSourceManagerImpl" format="xml" multifile-model="true">
<data-source source="LOCAL" name="langchain4j@localhost" uuid="f6ee985e-a235-49a0-a77a-ef4678f524a9">
<driver-ref>mysql.8</driver-ref>
<synchronize>true</synchronize>
<jdbc-driver>com.mysql.cj.jdbc.Driver</jdbc-driver>
<jdbc-url>jdbc:mysql://localhost:3306/langchain4j</jdbc-url>
<jdbc-additional-properties>
<property name="com.intellij.clouds.kubernetes.db.host.port" />
<property name="com.intellij.clouds.kubernetes.db.enabled" value="false" />
<property name="com.intellij.clouds.kubernetes.db.container.port" />
</jdbc-additional-properties>
<working-dir>$ProjectFileDir$</working-dir>
</data-source>
</component>
</project>

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="Encoding">
<file url="file://$PROJECT_DIR$/langchain4j-ai-helloworld/src/main/java" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-helloworld/src/main/resources" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-image/src/main/java" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-image/src/main/resources" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-low-high-api/src/main/java" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-low-high-api/src/main/resources" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-memory/src/main/java" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-memory/src/main/resources" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-model-params/src/main/java" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-model-params/src/main/resources" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-mongodb/src/main/java" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-mongodb/src/main/resources" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-multimode/src/main/java" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-multimode/src/main/resources" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-pinecone/src/main/java" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-pinecone/src/main/resources" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-prompt/src/main/java" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-prompt/src/main/resources" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-rag/src/main/java" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-rag/src/main/resources" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-stream/src/main/java" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-stream/src/main/resources" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-tools/src/main/java" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-tools/src/main/resources" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-xiaoai-agent/src/main/java" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/langchain4j-ai-xiaoai-agent/src/main/resources" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/src/main/java" charset="UTF-8" />
<file url="file://$PROJECT_DIR$/src/main/resources" charset="UTF-8" />
<file url="file://$APPLICATION_HOME_DIR$/bin/src/main/java" charset="UTF-8" />
<file url="file://$APPLICATION_HOME_DIR$/bin/src/main/resources" charset="UTF-8" />
</component>
</project>

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ExternalStorageConfigurationManager" enabled="true" />
<component name="MavenProjectsManager">
<option name="originalFiles">
<list>
<option value="$PROJECT_DIR$/pom.xml" />
</list>
</option>
</component>
<component name="ProjectRootManager" version="2" languageLevel="JDK_17" default="true" project-jdk-name="17" project-jdk-type="JavaSDK">
<output url="file://$PROJECT_DIR$/out" />
</component>
</project>

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langchain4j-ai-helloworld/pom.xml Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.iwe3</groupId>
<artifactId>langchain4j-ai-java</artifactId>
<version>1.0-SNAPSHOT</version>
</parent>
<artifactId>langchain4j-ai-helloworld</artifactId>
<packaging>jar</packaging>
<properties>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai</artifactId>
</dependency>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j</artifactId>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<!--test-->
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<scope>test</scope>
</dependency>
</dependencies>
</project>

12
langchain4j-ai-helloworld/src/main/java/com/iwe3/langchain4j/HelloworldApplication.java Normal file
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package com.iwe3.langchain4j;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class HelloworldApplication {
public static void main(String[] args) {
SpringApplication.run(HelloworldApplication.class,args);
}
}

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langchain4j-ai-helloworld/src/main/java/com/iwe3/langchain4j/config/LLMConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class LLMConfig {
@Bean
public ChatModel chatModelQwen(){
return OpenAiChatModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.modelName("qwen-plus")
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
}
}

26
langchain4j-ai-helloworld/src/main/java/com/iwe3/langchain4j/controller/LangChain4JController.java Normal file
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package com.iwe3.langchain4j.controller;
import dev.langchain4j.model.chat.ChatModel;
import jakarta.annotation.Resource;
import lombok.extern.slf4j.Slf4j;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
@RestController
@Slf4j
public class LangChain4JController
{
// http://localhost:9001/langchain4j/hello?question=如何学习LangChain4J
@Resource
private ChatModel chatModel;
@GetMapping(value = "/langchain4j/hello")
public String hello(@RequestParam(value = "question",defaultValue = "你是谁") String question)
{
var result = chatModel.chat(question);
System.out.println("通过langchain4j调用模型返回结果：\n"+result);
return result;
}
}

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langchain4j-ai-helloworld/src/main/resources/application.yml Normal file
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server:
port: 9001
spring:
application:
name: langchain4j-ai-helloworld

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langchain4j-ai-image/pom.xml Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.iwe3</groupId>
<artifactId>langchain4j-ai-java</artifactId>
<version>1.0-SNAPSHOT</version>
</parent>
<artifactId>langchain4j-ai-image</artifactId>
<properties>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<!--DashScope (Qwen)接入阿里云百炼平台
https://docs.langchain4j.dev/integrations/language-models/dashscope
-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-community-dashscope-spring-boot-starter</artifactId>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!--导入低阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<!--导入高阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
</dependencies>
</project>

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langchain4j-ai-image/src/main/java/com/iwe3/langchain4j/ImageApplication.java Normal file
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package com.iwe3.langchain4j;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class ImageApplication {
public static void main(String[] args) {
SpringApplication.run(ImageApplication.class,args);
}
}

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langchain4j-ai-image/src/main/java/com/iwe3/langchain4j/config/LLMConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.community.model.dashscope.WanxImageModel;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class LLMConfig
{
@Bean
public ChatModel ImageModel() {
return OpenAiChatModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
//qwen-vl-max 是一个多模态大模型，支持图片和文本的结合输入，适用于视觉-语言任务。
.modelName("qwen-vl-max")
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
}
/**
* @Description: 测试通义万象来实现图片生成，
* 知识出处，https://help.aliyun.com/zh/model-studio/text-to-image
*/
@Bean
public WanxImageModel wanxImageModel()
{
return WanxImageModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.modelName("wanx2.1-t2i-turbo") //图片生成 https://help.aliyun.com/zh/model-studio/text-to-image
.build();
}
}

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langchain4j-ai-image/src/main/java/com/iwe3/langchain4j/controller/ImageModelController.java Normal file
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package com.iwe3.langchain4j.controller;
import dev.langchain4j.data.message.ImageContent;
import dev.langchain4j.data.message.TextContent;
import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.chat.response.ChatResponse;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.core.io.Resource;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;
import java.io.IOException;
import java.util.Base64;
@RestController
@Slf4j
public class ImageModelController
{
@Autowired
private ChatModel ImageModel;
@Value("classpath:static/imgs/1.png")
private Resource resource;//import org.springframework.core.io.Resource;
/**
* @Description:
* 通过Base64编码将图片转化为字符串
* 结合ImageContent和TextContent形成UserMessage一起发送到模型进行处理。
*
*测试地址：http://localhost:9005/lc4j/image/call
*/
@GetMapping(value = "/lc4j/image/call")
public String readImageContent() throws IOException
{
String result = null;
//第一步，图片转码：通过Base64编码将图片转化为字符串
byte[] byteArray = resource.getContentAsByteArray();
String base64Data = Base64.getEncoder().encodeToString(byteArray);
//第二步，提示词指定：结合ImageContent和TextContent一起发送到模型进行处理。
UserMessage userMessage = UserMessage.from(
TextContent.from("她是谁？"),
ImageContent.from(base64Data, "image/jpg")
);
//第三步，API调用：使用OpenAiChatModel来构建请求，并通过chat()方法调用模型。
//请求内容包括文本提示和图片，模型会根据输入返回分析结果。
ChatResponse chatResponse = ImageModel.chat(userMessage);
//第四步，解析与输出：从ChatResponse中获取AI大模型的回复，打印出处理后的结果。
result = chatResponse.aiMessage().text();
//后台打印
System.out.println(result);
//返回前台
return result;
}
}

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langchain4j-ai-image/src/main/java/com/iwe3/langchain4j/controller/WanxImageModelController.java Normal file
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package com.iwe3.langchain4j.controller;
import com.alibaba.dashscope.aigc.imagesynthesis.ImageSynthesis;
import com.alibaba.dashscope.aigc.imagesynthesis.ImageSynthesisParam;
import com.alibaba.dashscope.aigc.imagesynthesis.ImageSynthesisResult;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.utils.JsonUtils;
import dev.langchain4j.community.model.dashscope.WanxImageModel;
import dev.langchain4j.data.image.Image;
import dev.langchain4j.model.output.Response;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;
import java.io.IOException;
@RestController
@Slf4j
public class WanxImageModelController
{
@Autowired
private WanxImageModel wanxImageModel;
// http://localhost:9005/lc4j/image/create
@GetMapping(value = "/lc4j/image/create")
public String createImageContent() throws IOException
{
System.out.println(wanxImageModel);
var imageResponse = wanxImageModel.generate("美女");
System.out.println(imageResponse.content().url());
return imageResponse.content().url().toString();
}
// http://localhost:9005/lc4j/image/create2
@GetMapping(value = "/lc4j/image/create2")
public String createImageContent2() throws IOException
{
var prompt = "近景镜头，18岁的中国女孩，古代服饰，圆脸，正面看着镜头，" +
"民族优雅的服装，商业摄影，室外，电影级光照，半身特写，精致的淡妆，锐利的边缘。";
var param =
ImageSynthesisParam.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.model(ImageSynthesis.Models.WANX_V1)
.prompt(prompt)
.style("<watercolor>")
.n(1)
.size("1024*1024")
.build();
var imageSynthesis = new ImageSynthesis();
ImageSynthesisResult result = null;
try {
System.out.println("---sync call, please wait a moment----");
result = imageSynthesis.call(param);
} catch (ApiException | NoApiKeyException e){
throw new RuntimeException(e.getMessage());
}
System.out.println(JsonUtils.toJson(result));
return JsonUtils.toJson(result);
}
}

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langchain4j-ai-image/src/main/resources/application.yml Normal file
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server:
port: 9005
spring:
application:
name: langchain4j-ai-image

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langchain4j-ai-low-high-api/pom.xml Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.iwe3</groupId>
<artifactId>langchain4j-ai-java</artifactId>
<version>1.0-SNAPSHOT</version>
</parent>
<artifactId>langchain4j-ai-low-high-api</artifactId>
<properties>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!--导入低阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<!--导入高阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
</dependencies>
</project>

12
langchain4j-ai-low-high-api/src/main/java/com/iwe3/langchain4j/LowHighApplication.java Normal file
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package com.iwe3.langchain4j;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class LowHighApplication {
public static void main(String[] args) {
SpringApplication.run(LowHighApplication.class,args);
}
}

37
langchain4j-ai-low-high-api/src/main/java/com/iwe3/langchain4j/config/LLMConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class LLMConfig {
@Bean(name = "qwen")
public ChatModel chatModelQwen(){
/*大模型3件套：apikey ,model-name,base-url */
return OpenAiChatModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.modelName("qwen-plus")
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
}
/**
* @Description:
* 知识出处，https://api-docs.deepseek.com/zh-cn/
*/
@Bean(name = "deepseek")
public ChatModel chatModelDeepSeek()
{
return
OpenAiChatModel.builder()
.apiKey(System.getenv("DEEP_SEEK_API_KEY"))
.modelName("deepseek-chat")
//.modelName("deepseek-reasoner")
.baseUrl("https://api.deepseek.com/v1")
.build();
}
}

21
langchain4j-ai-low-high-api/src/main/java/com/iwe3/langchain4j/controller/HighLevelController.java Normal file
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package com.iwe3.langchain4j.controller;
import com.iwe3.langchain4j.service.ChatAssistant;
import jakarta.annotation.Resource;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
@RestController
public class HighLevelController {
@Resource
private ChatAssistant chatAssistant;
// http://localhost:9003/lc4j/boot/declarative?prompt=我是谁
@GetMapping(value = "/lc4j/boot/declarative")
public String declarative(@RequestParam(value = "prompt", defaultValue = "你是谁") String prompt)
{
return chatAssistant.chat(prompt);
}
}

41
langchain4j-ai-low-high-api/src/main/java/com/iwe3/langchain4j/controller/LowLevelController.java Normal file
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package com.iwe3.langchain4j.controller;
import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.chat.response.ChatResponse;
import dev.langchain4j.model.output.TokenUsage;
import jakarta.annotation.Resource;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
@RestController
public class LowLevelController {
@Resource(name = "qwen")
private ChatModel chatModel;
// http://localhost:9003/lc4j/lowapi/api01?prompt=我是谁
@GetMapping(value = "/lc4j/lowapi/api01")
public String api01(@RequestParam(value = "prompt", defaultValue = "你是谁") String prompt)
{
return chatModel.chat(prompt);
}
// http://localhost:9003/lc4j/lowapi/api02?prompt=我是谁
@GetMapping(value = "/lc4j/lowapi/api02")
public String api02(@RequestParam(value = "prompt", defaultValue = "你是谁") String prompt)
{
ChatResponse chatResponse = chatModel.chat(UserMessage.from(prompt));
String result = chatResponse.aiMessage().text();
System.out.println("通过调用大模型返回结果："+result);
// Token 用量计算的底层api
TokenUsage tokenUsage = chatResponse.tokenUsage();
System.out.println("本次调用消耗的token："+tokenUsage);
result = result +"\t\n"+tokenUsage;
return result;
}
}

14
langchain4j-ai-low-high-api/src/main/java/com/iwe3/langchain4j/service/ChatAssistant.java Normal file
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package com.iwe3.langchain4j.service;
import dev.langchain4j.service.spring.AiService;
import dev.langchain4j.service.spring.AiServiceWiringMode;
/**
* 知识出处:
* https://docs.langchain4j.dev/tutorials/spring-boot-integration/#spring-boot-starter-for-declarative-ai-services
*/
@AiService(wiringMode = AiServiceWiringMode.EXPLICIT
,chatModel = "qwen")
public interface ChatAssistant {
String chat(String prompt);
}

5
langchain4j-ai-low-high-api/src/main/resources/application.yml Normal file
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server:
port: 9003
spring:
application:
name: langchain4j-ai-low-level

52
langchain4j-ai-memory/pom.xml Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.iwe3</groupId>
<artifactId>langchain4j-ai-java</artifactId>
<version>1.0-SNAPSHOT</version>
</parent>
<artifactId>langchain4j-ai-memory</artifactId>
<properties>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-reactor</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!--导入低阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<!--导入高阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
</dependency>
</dependencies>
</project>

12
langchain4j-ai-memory/src/main/java/com/iwe3/langchain4j/MemoryApplication.java Normal file
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package com.iwe3.langchain4j;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class MemoryApplication {
public static void main(String[] args) {
SpringApplication.run(MemoryApplication.class,args);
}
}

23
langchain4j-ai-memory/src/main/java/com/iwe3/langchain4j/config/LLMConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class LLMConfig {
/**
* @Description: 普通对话接口 ChatModel
*/
@Bean(name = "qwen")
public ChatModel chatModelQwen()
{
return OpenAiChatModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.modelName("qwen-plus")
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
}
}

19
langchain4j-ai-memory/src/main/java/com/iwe3/langchain4j/config/MemoryChatAssistantConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.memory.ChatMemory;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class MemoryChatAssistantConfig {
//@Bean -> 向IOC容器中注入 chatMemory 的实例
@Bean
ChatMemory windowChatMemory() {
// 设置聊天记忆记录的message数量
return MessageWindowChatMemory.withMaxMessages(10);
}
}

26
langchain4j-ai-memory/src/main/java/com/iwe3/langchain4j/config/SeparateChatAssistantConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.store.memory.chat.InMemoryChatMemoryStore;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class SeparateChatAssistantConfig {
/**
* 聊天记忆提供器
* 配置：采用memoryId来完成隔离
* @return
*/
@Bean
ChatMemoryProvider chatMemoryProvider() {
return memoryId -> MessageWindowChatMemory.builder()
.id(memoryId)
.maxMessages(10).chatMemoryStore(new InMemoryChatMemoryStore())
.build();
//如果未来想自定义 -> 则自己写一个类实现 ChatMemoryStore
}
}

20
langchain4j-ai-memory/src/main/java/com/iwe3/langchain4j/service/ChatAssistant.java Normal file
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package com.iwe3.langchain4j.service;
import dev.langchain4j.service.spring.AiService;
import dev.langchain4j.service.spring.AiServiceWiringMode;
/**
* 知识出处:
* https://docs.langchain4j.dev/tutorials/spring-boot-integration/#spring-boot-starter-for-declarative-ai-services
* chatMemory = "chatMemory" 则表示：使用ChatMemory来完成聊天记忆
*/
@AiService(wiringMode = AiServiceWiringMode.EXPLICIT
,chatModel = "qwen",chatMemory = "windowChatMemory")
public interface ChatAssistant {
/**
* 普通聊天
* @param prompt
* @return
*/
String chat(String prompt);
}

22
langchain4j-ai-memory/src/main/java/com/iwe3/langchain4j/service/SeparateChatAssistant.java Normal file
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package com.iwe3.langchain4j.service;
import dev.langchain4j.service.MemoryId;
import dev.langchain4j.service.UserMessage;
import dev.langchain4j.service.spring.AiService;
import dev.langchain4j.service.spring.AiServiceWiringMode;
@AiService(
wiringMode = AiServiceWiringMode.EXPLICIT,
chatModel = "qwen",
chatMemoryProvider = "chatMemoryProvider"
)
public interface SeparateChatAssistant {
/**
* 分离聊天记录
* @param memoryId 聊天id
* @param userMessage 用户消息
* @return
*/
String chat(@MemoryId int memoryId, @UserMessage String userMessage);
}

10
langchain4j-ai-memory/src/main/resources/application.yml Normal file
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server:
port: 9007
servlet:
encoding:
charset: utf-8
enabled: true
force: true # 设置响应的字符编码，避免流式返回输出乱码
spring:
application:
name: langchain4j-ai-memory

51
langchain4j-ai-memory/src/test/java/com/iwe3/langchain4j/MemoryApplicationTest.java Normal file
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package com.iwe3.langchain4j;
import com.iwe3.langchain4j.service.ChatAssistant;
import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.model.chat.ChatModel;
import jakarta.annotation.Resource;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
@SpringBootTest
class MemoryApplicationTest {
@Resource(name="qwen")
private ChatModel chatModel;
@Test
public void testLowerLevel(){
// 第一轮对话
var um01 = UserMessage.userMessage("我是蒲哥");
var res01 = chatModel.chat(um01);
var am01 = res01.aiMessage();
// 输出大语言模型的回复
System.out.println(am01.text());
// 第二轮对话
var um02 = UserMessage.userMessage("你知道我是谁吗");
//将第一轮对话记录，也一起发给 LLM
var res02 = chatModel.chat(um02);
var am02 = res02.aiMessage();
// 输出大语言模型的回复
System.out.println(am02.text());
}
@Autowired
private ChatAssistant chatAssistant;
@Test
public void testHighLevel(){
var res = chatAssistant.chat("我是蒲哥");
System.out.println("res = " + res);
res = chatAssistant.chat("我是谁？");
System.out.println("res = " + res);
}
}

30
langchain4j-ai-memory/src/test/java/com/iwe3/langchain4j/SeparateChatTest.java Normal file
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package com.iwe3.langchain4j;
import com.iwe3.langchain4j.service.SeparateChatAssistant;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
@SpringBootTest
public class SeparateChatTest {
@Autowired
private SeparateChatAssistant separateChatAssistant;
@Test
public void testSeparateChatMemory() {
// 用户1的对话
var res = separateChatAssistant.chat(1, "我是蒲哥");
System.out.println(res);
res = separateChatAssistant.chat(1, "你知道我是谁吗");
System.out.println(res);
// 用户2的对话
var res02 = separateChatAssistant.chat(2, "我是小明");
System.out.println(res02);
res02 = separateChatAssistant.chat(2, "我叫什么名字");
System.out.println(res02);
}
}

51
langchain4j-ai-model-params/pom.xml Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.iwe3</groupId>
<artifactId>langchain4j-ai-java</artifactId>
<version>1.0-SNAPSHOT</version>
</parent>
<artifactId>langchain4j-ai-model-params</artifactId>
<properties>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<dependency>
<groupId>ch.qos.logback</groupId>
<artifactId>logback-classic</artifactId>
</dependency>
<dependency>
<groupId>cn.hutool</groupId>
<artifactId>hutool-all</artifactId>
<version>5.8.39</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!--导入低阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<!--导入高阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
</dependencies>
</project>

12
langchain4j-ai-model-params/src/main/java/com/iwe3/langchain4j/ModelParamsApplication.java Normal file
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package com.iwe3.langchain4j;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class ModelParamsApplication {
public static void main(String[] args) {
SpringApplication.run(ModelParamsApplication.class,args);
}
}

45
langchain4j-ai-model-params/src/main/java/com/iwe3/langchain4j/config/LLMConfig.java Normal file
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package com.iwe3.langchain4j.config;
import com.iwe3.langchain4j.listener.UserpuChatModelListener;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import java.time.Duration;
import java.util.List;
@Configuration
public class LLMConfig {
@Bean(name = "qwen")
public ChatModel chatModelQwen(){
/*模型3件套：apikey - modelName - baseUrl */
return OpenAiChatModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.modelName("qwen-plus")
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.logRequests(true)
.logResponses(true)
.listeners(List.of(new UserpuChatModelListener()))
.maxRetries(3)
.timeout(Duration.ofSeconds(5))
.build();
}
/**
* @Description:
* 知识出处，https://api-docs.deepseek.com/zh-cn/
*/
@Bean(name = "deepseek")
public ChatModel chatModelDeepSeek(){
return
OpenAiChatModel.builder()
.apiKey(System.getenv("DEEP_SEEK_API_KEY"))
.modelName("deepseek-chat")
//.modelName("deepseek-reasoner")
.baseUrl("https://api.deepseek.com/v1")
.build();
}
}

27
langchain4j-ai-model-params/src/main/java/com/iwe3/langchain4j/controller/ModelParameterController.java Normal file
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package com.iwe3.langchain4j.controller;
import dev.langchain4j.model.chat.ChatModel;
import jakarta.annotation.Resource;
import lombok.extern.slf4j.Slf4j;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
@RestController
@Slf4j
public class ModelParameterController
{
@Resource(name = "qwen")
private ChatModel chatModelQwen;
// http://localhost:9004/lc4j/modelparam/config?prompt=我是谁
@GetMapping(value = "/lc4j/modelparam/config")
public String config(@RequestParam(value = "prompt", defaultValue = "你是谁") String prompt)
{
var result = chatModelQwen.chat(prompt);
System.out.println("通过langchain4j调用模型返回结果："+result);
return result;
}
}

36
langchain4j-ai-model-params/src/main/java/com/iwe3/langchain4j/listener/UserpuChatModelListener.java Normal file
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package com.iwe3.langchain4j.listener;
import cn.hutool.core.util.IdUtil;
import dev.langchain4j.model.chat.listener.ChatModelErrorContext;
import dev.langchain4j.model.chat.listener.ChatModelListener;
import dev.langchain4j.model.chat.listener.ChatModelRequestContext;
import dev.langchain4j.model.chat.listener.ChatModelResponseContext;
import lombok.extern.slf4j.Slf4j;
@Slf4j
public class UserpuChatModelListener implements ChatModelListener
{
@Override
public void onRequest(ChatModelRequestContext requestContext)
{
// onRequest配置的k:v键值对，在onResponse阶段可以获得，上下文传递参数好用
String uuidValue = IdUtil.simpleUUID();
requestContext.attributes().put("TraceID",uuidValue);
log.info("请求参数requestContext:{}", requestContext+"\t"+uuidValue);
}
@Override
public void onResponse(ChatModelResponseContext responseContext)
{
Object object = responseContext.attributes().get("TraceID");
log.info("返回结果responseContext:{}", object);
}
@Override
public void onError(ChatModelErrorContext errorContext)
{
log.error("请求异常ChatModelErrorContext:{}", errorContext);
}
}

9
langchain4j-ai-model-params/src/main/resources/application.yml Normal file
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server:
port: 9004
spring:
application:
name: langchain4j-ai-model-params
logging:
level:
dev:
langchain4j: debug

57
langchain4j-ai-mongodb/pom.xml Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.iwe3</groupId>
<artifactId>langchain4j-ai-java</artifactId>
<version>1.0-SNAPSHOT</version>
</parent>
<artifactId>langchain4j-ai-mongodb</artifactId>
<properties>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<!-- Spring Boot Starter Data MongoDB -->
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-data-mongodb</artifactId>
</dependency>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-reactor</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!--导入低阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<!--导入高阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
</dependency>
</dependencies>
</project>

12
langchain4j-ai-mongodb/src/main/java/com/iwe3/langchain4j/MongoDBApplication.java Normal file
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package com.iwe3.langchain4j;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class MongoDBApplication {
public static void main(String[] args) {
SpringApplication.run(MongoDBApplication.class,args);
}
}

23
langchain4j-ai-mongodb/src/main/java/com/iwe3/langchain4j/config/LLMConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class LLMConfig {
/**
* @Description: 普通对话接口 ChatModel
*/
@Bean(name = "qwen")
public ChatModel chatModelQwen()
{
return OpenAiChatModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.modelName("qwen-plus")
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
}
}

51
langchain4j-ai-mongodb/src/main/java/com/iwe3/langchain4j/config/MongoChatMemoryStore.java Normal file
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package com.iwe3.langchain4j.config;
import com.iwe3.langchain4j.entity.ChatMessages;
import dev.langchain4j.data.message.ChatMessage;
import dev.langchain4j.data.message.ChatMessageDeserializer;
import dev.langchain4j.data.message.ChatMessageSerializer;
import dev.langchain4j.store.memory.chat.ChatMemoryStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.data.mongodb.core.MongoTemplate;
import org.springframework.data.mongodb.core.query.Criteria;
import org.springframework.data.mongodb.core.query.Query;
import org.springframework.data.mongodb.core.query.Update;
import org.springframework.stereotype.Component;
import java.util.LinkedList;
import java.util.List;
@Component
public class MongoChatMemoryStore implements ChatMemoryStore {
@Autowired
private MongoTemplate mongoTemplate;
@Override
public List<ChatMessage> getMessages(Object memoryId) {
Criteria criteria = Criteria.where("memoryId").is(memoryId);
Query query = new Query(criteria);
ChatMessages chatMessages = mongoTemplate.findOne(query, ChatMessages.class);
if (chatMessages == null) return new LinkedList<>();
return ChatMessageDeserializer.messagesFromJson(chatMessages.getContent());
}
@Override
public void updateMessages(Object memoryId, List<ChatMessage> messages) {
Criteria criteria = Criteria.where("memoryId").is(memoryId);
Query query = new Query(criteria);
Update update = new Update();
update.set("content", ChatMessageSerializer.messagesToJson(messages));
// 根据query条件能查询出文档，则修改文档；否则新增文档
mongoTemplate.upsert(query, update, ChatMessages.class);
}
@Override
public void deleteMessages(Object memoryId) {
Criteria criteria = Criteria.where("memoryId").is(memoryId);
Query query = new Query(criteria);
mongoTemplate.remove(query, ChatMessages.class);
}
}

29
langchain4j-ai-mongodb/src/main/java/com/iwe3/langchain4j/config/SeparateChatAssistantConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.store.memory.chat.InMemoryChatMemoryStore;
import jakarta.annotation.Resource;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class SeparateChatAssistantConfig {
@Resource
private MongoChatMemoryStore mongoChatMemoryStore;
/**
* 指定使用-> mongoChatMemoryStore 来存储
* @return
*/
@Bean
ChatMemoryProvider chatMemoryProvider() {
return memoryId -> MessageWindowChatMemory.builder()
.id(memoryId)
.maxMessages(10).
chatMemoryStore(mongoChatMemoryStore)
.build();
}
}

24
langchain4j-ai-mongodb/src/main/java/com/iwe3/langchain4j/entity/ChatMessages.java Normal file
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package com.iwe3.langchain4j.entity;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.bson.types.ObjectId;
import org.springframework.data.annotation.Id;
import org.springframework.data.mongodb.core.mapping.Document;
@Data
@AllArgsConstructor
@NoArgsConstructor
@Document("chat_messages")
public class ChatMessages {
//唯一标识，映射到 MongoDB 文档的 _id 字段
@Id
private ObjectId messageId; //利用数据库生成ID就用ObjectId
private String memoryId; //隔离聊天记录的ID
private String content; //存储当前聊天记录列表的json字符串
}

22
langchain4j-ai-mongodb/src/main/java/com/iwe3/langchain4j/service/SeparateChatAssistant.java Normal file
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package com.iwe3.langchain4j.service;
import dev.langchain4j.service.MemoryId;
import dev.langchain4j.service.UserMessage;
import dev.langchain4j.service.spring.AiService;
import dev.langchain4j.service.spring.AiServiceWiringMode;
@AiService(
wiringMode = AiServiceWiringMode.EXPLICIT,
chatModel = "qwen",
chatMemoryProvider = "chatMemoryProvider"
)
public interface SeparateChatAssistant {
/**
* 分离聊天记录
* @param memoryId 聊天id
* @param userMessage 用户消息
* @return
*/
String chat(@MemoryId int memoryId, @UserMessage String userMessage);
}

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langchain4j-ai-mongodb/src/main/resources/application.yml Normal file
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server:
port: 9008
servlet:
encoding:
charset: utf-8
enabled: true
force: true # 设置响应的字符编码，避免流式返回输出乱码
spring:
application:
name: langchain4j-ai-mongodb
data:
mongodb:
uri: mongodb://localhost:27017/chat_memory_db # MongoDB连接配置,数据库 chat_memory_db 会自动创建

57
langchain4j-ai-mongodb/src/test/java/com/iwe3/langchain4j/MongoCrudTest.java Normal file
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package com.iwe3.langchain4j;
import com.iwe3.langchain4j.entity.ChatMessages;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.data.mongodb.core.MongoTemplate;
import org.springframework.data.mongodb.core.query.Criteria;
import org.springframework.data.mongodb.core.query.Query;
import org.springframework.data.mongodb.core.query.Update;
@SpringBootTest
public class MongoCrudTest {
@Autowired
private MongoTemplate mongoTemplate;
@Test
public void delete(){
var criteria = Criteria.where("_id").is("69420db67db051baab1b4873");
var query = new Query(criteria);
mongoTemplate.remove(query,ChatMessages.class);
}
@Test
public void update(){
//id不存在，则会自动变成：新增
var criteria = Criteria.where("_id").is("69420db67db051baab1b4873");
var query = new Query(criteria);
//创建一个修改对象
var update = new Update();
update.set("content","奥特之母");
//将查询条件， 修改的内容，文档的信息 -> mongodb
mongoTemplate.upsert(query,update,ChatMessages.class);
}
@Test
public void insert(){
// mongoTemplate.insert(new ChatMessages(1L,"迪迦")); //程序员自己管理ID就用Long
//利用数据库生成ID就用ObjectId
var msg = new ChatMessages();
msg.setContent("贝利亚");
mongoTemplate.insert(msg);
}
@Test
public void findById(){
var msg = mongoTemplate.
findById("69420db67db051baab1b4873",
ChatMessages.class);
System.out.println(msg);
}
}

30
langchain4j-ai-mongodb/src/test/java/com/iwe3/langchain4j/MongoDBApplicationTest.java Normal file
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package com.iwe3.langchain4j;
import com.iwe3.langchain4j.service.SeparateChatAssistant;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
@SpringBootTest
class MongoDBApplicationTest {
@Autowired
private SeparateChatAssistant separateChatAssistant;
@Test
public void testSeparateChatMemory() {
// 用户1的对话
var res = separateChatAssistant.chat(1, "我是蒲哥");
System.out.println(res);
res = separateChatAssistant.chat(1, "你知道我是谁吗");
System.out.println(res);
// 用户2的对话
var res02 = separateChatAssistant.chat(2, "我是小明");
System.out.println(res02);
res02 = separateChatAssistant.chat(2, "我叫什么名字");
System.out.println(res02);
}
}

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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.iwe3</groupId>
<artifactId>langchain4j-ai-java</artifactId>
<version>1.0-SNAPSHOT</version>
</parent>
<artifactId>langchain4j-ai-multimode</artifactId>
<properties>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai</artifactId>
</dependency>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j</artifactId>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<!--test-->
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<scope>test</scope>
</dependency>
</dependencies>
</project>

11
langchain4j-ai-multimode/src/main/java/com/iwe3/langchain4j/MultiModelApplication.java Normal file
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package com.iwe3.langchain4j;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class MultiModelApplication {
public static void main(String[] args) {
SpringApplication.run(MultiModelApplication.class);
}
}

37
langchain4j-ai-multimode/src/main/java/com/iwe3/langchain4j/config/LLMConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class LLMConfig {
@Bean(name = "qwen")
public ChatModel chatModelQwen()
{
return OpenAiChatModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.modelName("qwen-plus")
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
}
/**
* @Description:
* 知识出处，https://api-docs.deepseek.com/zh-cn/
*/
@Bean(name = "deepseek")
public ChatModel chatModelDeepSeek()
{
return
OpenAiChatModel.builder()
.apiKey(System.getenv("DEEP_SEEK_API_KEY"))
.modelName("deepseek-chat")
//.modelName("deepseek-reasoner")
.baseUrl("https://api.deepseek.com/v1")
.build();
}
}

35
langchain4j-ai-multimode/src/main/java/com/iwe3/langchain4j/controller/MultiModelController.java Normal file
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package com.iwe3.langchain4j.controller;
import dev.langchain4j.model.chat.ChatModel;
import jakarta.annotation.Resource;
import lombok.extern.slf4j.Slf4j;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
@RestController
@Slf4j
public class MultiModelController
{
@Resource(name = "qwen")
private ChatModel chatModelQwen;
@Resource(name = "deepseek")
private ChatModel chatModelDeepSeek;
// http://localhost:9002/multimodel/qwen?prompt=如何学习LangChain4J
@GetMapping(value = "/multimodel/qwen")
public String qwenCall(@RequestParam(value = "prompt", defaultValue = "你是谁") String prompt) {
var result = chatModelQwen.chat(prompt);
System.out.println("通过langchain4j调用模型返回结果：\n"+result);
return result;
}
// http://localhost:9002/multimodel/deepseek?prompt=如何学习LangChain4J
@GetMapping(value = "/multimodel/deepseek")
public String deepseekCall(@RequestParam(value = "prompt", defaultValue = "你是谁") String prompt){
var result = chatModelDeepSeek.chat(prompt);
System.out.println("通过langchain4j调用模型返回结果：\n"+result);
return result;
}
}

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langchain4j-ai-multimode/src/main/resources/application.yml Normal file
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server:
port: 9002
spring:
application:
name: langchain4j-ai-multimode

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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.iwe3</groupId>
<artifactId>langchain4j-ai-java</artifactId>
<version>1.0-SNAPSHOT</version>
</parent>
<artifactId>langchain4j-ai-pinecone</artifactId>
<properties>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-pinecone</artifactId>
</dependency>
<!-- 接入阿里云百炼平台 -->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-community-dashscope-spring-boot-starter</artifactId>
</dependency>
<!-- 简单的rag实现 -->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-easy-rag</artifactId>
</dependency>
<!-- 解析pdf文档 -->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-document-parser-apache-pdfbox</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!--导入低阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<!--导入高阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
</dependencies>
</project>

12
langchain4j-ai-pinecone/src/main/java/com/iwe3/langchain4j/PineConeApplication.java Normal file
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package com.iwe3.langchain4j;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class PineConeApplication {
public static void main(String[] args) {
SpringApplication.run(PineConeApplication.class,args);
}
}

32
langchain4j-ai-pinecone/src/main/java/com/iwe3/langchain4j/config/EmbeddingStoreConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.pinecone.PineconeEmbeddingStore;
import dev.langchain4j.store.embedding.pinecone.PineconeServerlessIndexConfig;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class EmbeddingStoreConfig {
@Autowired
private EmbeddingModel embeddingModel;
@Bean
public EmbeddingStore<TextSegment> embeddingStore() {
// 创建向量存储
return PineconeEmbeddingStore.builder()
.apiKey(System.getenv("PINECONE_API_KEY"))
.index("xiaoai-index") // 如果指定的索引不存在，将创建一个新的索引
.nameSpace("xiaoai-namespace") // 如果指定的名称空间不存在，将创建一个新的名称空间
.createIndex(PineconeServerlessIndexConfig.builder()
.cloud("AWS") // 指定索引部署在 AWS 云服务上。
.region("us-east-1") // 指定索引所在的 AWS 区域为 us-east-1。
.dimension(embeddingModel.dimension()) // 指定索引的向量维度，该维度与 embeddingModel 生成的向量维度相同。
.build())
.build();
}
}

23
langchain4j-ai-pinecone/src/main/java/com/iwe3/langchain4j/config/LLMConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class LLMConfig {
/**
* @Description: 普通对话接口 ChatModel
*/
@Bean(name = "qwen")
public ChatModel chatModelQwen()
{
return OpenAiChatModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.modelName("qwen-plus")
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
}
}

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langchain4j-ai-pinecone/src/main/resources/application.yml Normal file
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server:
port: 9011
spring:
application:
name: langchain4j-ai-pinecone
langchain4j:
community:
dashscope:
embedding-model:
model-name: text-embedding-v3
api-key: ${DASH_SCOPE_API_KEY}

79
langchain4j-ai-pinecone/src/test/java/com/iwe3/langchain4j/PineConeApplicationTest.java Normal file
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package com.iwe3.langchain4j;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingMatch;
import dev.langchain4j.store.embedding.EmbeddingSearchRequest;
import dev.langchain4j.store.embedding.EmbeddingSearchResult;
import dev.langchain4j.store.embedding.EmbeddingStore;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
@SpringBootTest
class PineConeApplicationTest {
@Autowired
private EmbeddingModel embeddingModel;
@Autowired
private EmbeddingStore embeddingStore;
/**
* Pinecone - 相似度匹配
*/
@Test
public void embeddingSearch() {
// 提问，并将问题转成向量数据
var queryEmbedding = embeddingModel.embed("你最喜欢的运动是什么？").content();
// 创建搜索请求对象
var searchRequest = EmbeddingSearchRequest.builder()
.queryEmbedding(queryEmbedding)
.maxResults(1) // 匹配最相似的一条记录
.minScore(0.8)
.build();
// 根据搜索请求 searchRequest 在向量存储中进行相似度搜索
EmbeddingSearchResult<TextSegment> searchResult = embeddingStore.search(searchRequest);
// searchResult.matches(): 获取搜索结果中的匹配项列表。
// .get(0): 从匹配项列表中获取第一个匹配项
EmbeddingMatch<TextSegment> embeddingMatch = searchResult.matches().get(0);
// 获取匹配项的相似度得分
System.out.println(embeddingMatch.score()); // 输出：0.8144288515898701
// 返回文本结果
System.out.println(embeddingMatch.embedded().text());
}
/**
* 将文本转换成向量，然后存储到 pinecone 中
*
* 参考:
* https://docs.langchain4j.dev/tutorials/embedding-stores
*/
@Test
public void testPineconeEmbedded() {
// 将文本转换成向量
var seg1 = TextSegment.from("我喜欢羽毛球");
var em1 = embeddingModel.embed(seg1).content();
embeddingStore.add(em1, seg1);// 存入向量数据库
var seg2 = TextSegment.from("今天天气很好");
var em2 = embeddingModel.embed(seg2).content();
embeddingStore.add(em2, seg2);// 存入向量数据库
}
@Test
public void testEmbeddingModel() {
var embed = embeddingModel.embed("你好");
System.out.println("向量维度：" + embed.content().vector().length);
System.out.println("向量输出：" + embed.toString());
}
}

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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.iwe3</groupId>
<artifactId>langchain4j-ai-java</artifactId>
<version>1.0-SNAPSHOT</version>
</parent>
<artifactId>langchain4j-ai-prompt</artifactId>
<properties>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!--导入低阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<!--导入高阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
</dependencies>
</project>

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langchain4j-ai-prompt/src/main/java/com/iwe3/langchain4j/PromptApplication.java Normal file
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package com.iwe3.langchain4j;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class PromptApplication {
public static void main(String[] args) {
SpringApplication.run(PromptApplication.class,args);
}
}

23
langchain4j-ai-prompt/src/main/java/com/iwe3/langchain4j/config/LLMConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class LLMConfig {
/**
* @Description: 普通对话接口 ChatModel
*/
@Bean(name = "qwen")
public ChatModel chatModelQwen()
{
return OpenAiChatModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.modelName("qwen-plus")
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
}
}

104
langchain4j-ai-prompt/src/main/java/com/iwe3/langchain4j/controller/ChatPromptController.java Normal file
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package com.iwe3.langchain4j.controller;
import com.iwe3.langchain4j.entity.LawPrompt;
import com.iwe3.langchain4j.service.LawExplainAssistant;
import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.chat.response.ChatResponse;
import dev.langchain4j.model.input.Prompt;
import dev.langchain4j.model.input.PromptTemplate;
import jakarta.annotation.Resource;
import lombok.extern.slf4j.Slf4j;
import org.assertj.core.util.DateUtil;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;
import java.util.Map;
@RestController
@Slf4j
public class ChatPromptController {
@Resource
private LawExplainAssistant lawExplainAssistant;
/**
* http://localhost:9008/lc4j/chatprompt/test2
* TRIPS协议（与贸易有关的知识产权协议）：
* 世界贸易组织（WTO）成员间的一个重要协议，
* 它规定了最低标准的知识产权保护要求，并适用于所有WTO成员。
* @return
*/
@GetMapping(value = "/lc4j/chatprompt/test2")
public String test2()
{
var prompt = new LawPrompt();
prompt.setLegal("知识产权");
prompt.setQuestion("TRIPS协议?");
var chat = lawExplainAssistant.chat(prompt);
System.out.println(chat);
return "success : "+ DateUtil.now()+"<br> \n\n chat: "+chat;
}
/**
* 高阶API调用
* http://localhost:9008/lc4j/chatprompt/test1
* @return
*/
@GetMapping(value = "/lc4j/chatprompt/test1")
public String test1()
{
String chat = lawExplainAssistant.chat("什么是知识产权？",2000);
System.out.println(chat);
String chat2 = lawExplainAssistant.chat("什么是java？",2000);
System.out.println(chat2);
return "success : "+ DateUtil.now()+"<br> \n\n chat: "+chat+"<br> \n\n chat2: "+chat2;
}
@Resource(name = "qwen")
private ChatModel chatModel;
/**
* 低阶API调用
* http://localhost:9008/lc4j/chatprompt/test
* @return
*/
@GetMapping(value = "/lc4j/chatprompt/test")
public String test(){
// 看看源码，默认 PromptTemplate 构造使用 it 属性作为默认占位符
/*String role = "外科医生";
String question = "牙疼";*/
String role = "财务会计";
String question = "人民币大写";
//1 构造PromptTemplate模板
PromptTemplate template = PromptTemplate.from("你是一个{{it}}助手,{{question}}怎么办");
//2 由PromptTemplate生成Prompt
Prompt prompt = template.apply(Map.of("it",role,"question",question));
//3 Prompt提示词变成UserMessage
UserMessage userMessage = prompt.toUserMessage();
//4 调用大模型
ChatResponse chatResponse = chatModel.chat(userMessage);
//4.1 后台打印
System.out.println(chatResponse.aiMessage().text());
//4.2 前台返回
return "success : "+ DateUtil.now()+"<br> \n\n chat: "+chatResponse.aiMessage().text();
}
}

12
langchain4j-ai-prompt/src/main/java/com/iwe3/langchain4j/entity/LawPrompt.java Normal file
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package com.iwe3.langchain4j.entity;
import dev.langchain4j.model.input.structured.StructuredPrompt;
import lombok.Data;
@Data
@StructuredPrompt("根据中国{{legal}}法律，解答以下问题：{{question}}")
public class LawPrompt
{
private String legal;//法律
private String question;//问题
}

38
langchain4j-ai-prompt/src/main/java/com/iwe3/langchain4j/service/LawExplainAssistant.java Normal file
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package com.iwe3.langchain4j.service;
import com.iwe3.langchain4j.entity.LawPrompt;
import dev.langchain4j.service.SystemMessage;
import dev.langchain4j.service.UserMessage;
import dev.langchain4j.service.V;
import dev.langchain4j.service.spring.AiService;
/**
* 一个法律解释助手
*/
@AiService(chatModel = "qwen")
public interface LawExplainAssistant {
//案例3 当提示词过多，也可以将从外部资源加载
@SystemMessage(fromResource = "prompt-template.txt")
String chat(LawPrompt lawPrompt);
//案例2 新建带着@StructuredPrompt的业务实体类，比如LawPrompt
// @SystemMessage("你是一位专业的中国法律顾问，只回答与中国法律相关的问题。" +
// "输出限制：对于其他领域的问题禁止回答，直接返回'抱歉，我只能回答中国法律相关的问题。'")
// String chat(LawPrompt lawPrompt);
// @SystemMessage : 定AI-LLM 的角色，任务，风格，格式
// @UserMessage : 用户提示词
// @V : 超过1个以上参数，需要起别名
//案例1 @SystemMessage + @UserMessage + @V
@SystemMessage("你是一位专业的中国法律顾问，只回答与中国法律相关的问题。" +
"输出限制：对于其他领域的问题禁止回答，直接返回'抱歉，我只能回答中国法律相关的问题。'")
@UserMessage("请回答以下法律问题：{{question}},字数控制在{{length}}以内")
String chat(@V("question") String question, @V("length") int length);
}

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langchain4j-ai-prompt/src/main/resources/application.yml Normal file
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server:
port: 9008
spring:
application:
name: langchain4j-ai-prompt

2
langchain4j-ai-prompt/src/main/resources/prompt-template.txt Normal file
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你是一位专业的中国法律顾问，只回答与中国法律相关的问题。
输出限制：对于其他领域的问题禁止回答，直接返回'抱歉，我只能回答中国法律相关的问题。

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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.iwe3</groupId>
<artifactId>langchain4j-ai-java</artifactId>
<version>1.0-SNAPSHOT</version>
</parent>
<artifactId>langchain4j-ai-rag</artifactId>
<properties>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<!-- 简单的rag实现 -->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-easy-rag</artifactId>
</dependency>
<!-- 解析pdf文档 -->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-document-parser-apache-pdfbox</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!--导入低阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<!--导入高阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
</dependencies>
</project>

12
langchain4j-ai-rag/src/main/java/com/iwe3/langchain4j/RagApplication.java Normal file
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package com.iwe3.langchain4j;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class RagApplication {
public static void main(String[] args) {
SpringApplication.run(RagApplication.class,args);
}
}

23
langchain4j-ai-rag/src/main/java/com/iwe3/langchain4j/config/LLMConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class LLMConfig {
/**
* @Description: 普通对话接口 ChatModel
*/
@Bean(name = "qwen")
public ChatModel chatModelQwen()
{
return OpenAiChatModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.modelName("qwen-plus")
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
}
}

5
langchain4j-ai-rag/src/main/resources/application.yml Normal file
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server:
port: 9010
spring:
application:
name: langchain4j-ai-rag

1
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电子科技大学，简称“成电”（UESTC） [12]，位于四川成都，是中华人民共和国教育部直属全国重点大学 [127]，国家首批“211工程”“985工程”重点建设大学，首批国家“双一流”建设高校； [188]入选111计划 [140]、101计划 [141]、强基计划 [137]、珠峰计划 [138]、英才计划 [120]、卓越工程师教育培养计划 [139]、国家建设高水平大学公派研究生项目 [136]；是一所以电子信息科学技术为核心，以工为主，理工渗透，理、工、管、文、医协调发展的多科性研究型大学，被誉为“中国民族电子工业摇篮”和“中国电子类院校排头兵”。

21
langchain4j-ai-rag/src/main/resources/files/电子科大.md Normal file
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电子科技大学（University of Electronic Science and Technology of China，简称 **UESTC** 或 **“成电”**）是中国电子信息领域顶尖的高等学府，也是国家“985工程”“211工程”和首批“双一流”A类建设高校。以下是其核心简介：
------
### 📍 基本信息
- **所在地**：四川省成都市（主校区为清水河校区，另有沙河、九里堤校区）
- **创办时间**：1956年
- **主管部门**：中华人民共和国教育部
- **校训**：求实求真，大气大为
------
### 🏛️ 历史沿革
- 由周恩来总理亲自部署，整合：
- 交通大学（现上海交大、西安交大）
- 南京工学院（现东南大学）
- 华南工学院（现华南理工大学） 的**电讯工程专业**，组建 **成都电讯工程学院**。
- 1988年更名为 **电子科技大学**。
- 1960年即被列为全国重点高校，1961年成为“国防七子”之一。

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83
langchain4j-ai-rag/src/test/java/com/iwe3/langchain4j/RagApplicationTest.java Normal file
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package com.iwe3.langchain4j;
import ai.djl.huggingface.tokenizers.HuggingFaceTokenizer;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.data.document.parser.apache.pdfbox.ApachePdfBoxDocumentParser;
import dev.langchain4j.data.document.splitter.DocumentByParagraphSplitter;
import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.chat.response.ChatResponse;
import dev.langchain4j.model.output.TokenUsage;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import jakarta.annotation.Resource;
import org.junit.jupiter.api.Test;
import org.springframework.boot.test.context.SpringBootTest;
@SpringBootTest
class RagApplicationTest {
@Resource
private ChatModel chatModel;
@Test
public void testCountToken(){
var res = chatModel.chat(UserMessage.from("你好，这是一段文字！"));
var result = res.aiMessage().text();
System.out.println("通过调用大模型返回结果：" + result);
/*Token用量计算底层API*/
var tu = res.tokenUsage();
System.out.println("本地调用销毁的TOKEN：" + tu);
}
/**
* 加载文档并存入向量数据库
*/
@Test
public void testReadDocumentAndStore() {
// 使用FileSystemDocumentLoader读取指定目录下的知识库文档
// 并使用默认的文档解析器TextDocumentParser对文档进行解析
var document = FileSystemDocumentLoader.loadDocument("D:/files/电子科大.md");
// 为了简单起见，我们暂时使用基于内存的向量存储
InMemoryEmbeddingStore<TextSegment> db = new InMemoryEmbeddingStore<>();
// /ingest
// /1、分割文档，默认使用递归分割器，将文档分割为多个文本片段，每个片段包含不超过 300个token，并且有 30个token的重叠部分保证连贯性
// /2、文本向量化：使用一个LangChain4j内置的轻量化向量模型对每个文本片段进行向量化
// /3、将原始文本和向量存储到向量数据库中(InMemoryEmbeddingStore)
EmbeddingStoreIngestor.ingest(document, db);
// 查看向量数据库内容
System.out.println(db);
}
/**
* 解析PDF
*/
@Test
public void testParsePDF() {
var document = FileSystemDocumentLoader.loadDocument(
"D:/files/电子科大.pdf",
new ApachePdfBoxDocumentParser()
);
System.out.println(document);
}
@Test
public void testReadDocument() {
// 使用FileSystemDocumentLoader读取指定目录下的知识库文档
// 并使用默认的文档解析器TextDocumentParser对文档进行解析
var document = FileSystemDocumentLoader
.loadDocument("D:/files/test.txt");
System.out.println(document.metadata());
System.out.println(document.text());
}
}

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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.iwe3</groupId>
<artifactId>langchain4j-ai-java</artifactId>
<version>1.0-SNAPSHOT</version>
</parent>
<artifactId>langchain4j-ai-stream</artifactId>
<properties>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
</dependency>
<!-- Spring Boot Starter Data MongoDB -->
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-data-mongodb</artifactId>
</dependency>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-reactor</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!--导入低阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<!--导入高阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
</dependencies>
</project>

13
langchain4j-ai-stream/src/main/java/com/iweb/langchain4j/StreamApplication.java Normal file
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package com.iweb.langchain4j;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class StreamApplication {
public static void main(String[] args) {
SpringApplication.run(StreamApplication.class,args);
}
}

36
langchain4j-ai-stream/src/main/java/com/iweb/langchain4j/config/LLMConfig.java Normal file
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package com.iweb.langchain4j.config;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.chat.StreamingChatModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import dev.langchain4j.model.openai.OpenAiStreamingChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class LLMConfig {
/**
* @Description: 普通对话接口 ChatModel
*/
@Bean(name = "qwen")
public ChatModel chatModelQwen()
{
return OpenAiChatModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.modelName("qwen-plus")
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
}
/**
* @Description: 流式对话接口 StreamingChatModel
*/
@Bean
public StreamingChatModel streamingChatModel(){
return OpenAiStreamingChatModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.modelName("qwen-plus")
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
}
}

57
langchain4j-ai-stream/src/main/java/com/iweb/langchain4j/controller/StreamController.java Normal file
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package com.iweb.langchain4j.controller;
import com.iweb.langchain4j.service.ChatAssistant;
import dev.langchain4j.model.chat.StreamingChatModel;
import dev.langchain4j.model.chat.response.ChatResponse;
import dev.langchain4j.model.chat.response.StreamingChatResponseHandler;
import jakarta.annotation.Resource;
import lombok.extern.slf4j.Slf4j;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import reactor.core.publisher.Flux;
@RestController
@Slf4j
public class StreamController {
@Resource //直接使用 low-level LLM API
private StreamingChatModel chatModelQwen;
@Resource //自己封装接口使用 high-level LLM API
private ChatAssistant chatAssistant;
// http://localhost:9006/lc4j/chatstream/chat2?prompt=我是谁？
@GetMapping(value = "/lc4j/chatstream/chat2")
public Flux<String> chat3(@RequestParam(value = "prompt", defaultValue = "你是谁？") String prompt) {
return chatAssistant.chatFlux(prompt);
}
// http://localhost:9006/lc4j/chatstream/chat?prompt=我是谁？
@GetMapping(value = "/lc4j/chatstream/chat")
public Flux<String> chat(@RequestParam("prompt") String prompt) {
return Flux.create(emitter -> {
chatModelQwen.chat(prompt, new StreamingChatResponseHandler()
{
@Override
public void onPartialResponse(String partialResponse)
{
emitter.next(partialResponse);
}
@Override
public void onCompleteResponse(ChatResponse completeResponse)
{
emitter.complete();
}
@Override
public void onError(Throwable throwable)
{
emitter.error(throwable);
}
});
});
}
}

27
langchain4j-ai-stream/src/main/java/com/iweb/langchain4j/service/ChatAssistant.java Normal file
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package com.iweb.langchain4j.service;
import dev.langchain4j.service.spring.AiService;
import dev.langchain4j.service.spring.AiServiceWiringMode;
import reactor.core.publisher.Flux;
/**
* 知识出处:
* https://docs.langchain4j.dev/tutorials/spring-boot-integration/#spring-boot-starter-for-declarative-ai-services
*/
@AiService(wiringMode = AiServiceWiringMode.EXPLICIT
,streamingChatModel = "streamingChatModel")
public interface ChatAssistant {
/**
* 普通聊天
* @param prompt
* @return
*/
String chat(String prompt);
/**
* 流式聊天
* @param prompt
* @return
*/
Flux<String> chatFlux(String prompt);
}

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langchain4j-ai-stream/src/main/resources/application.yml Normal file
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server:
port: 9006
servlet:
encoding:
charset: utf-8
enabled: true
force: true # 设置响应的字符编码，避免流式返回输出乱码
spring:
application:
name: langchain4j-ai-stream

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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.iwe3</groupId>
<artifactId>langchain4j-ai-java</artifactId>
<version>1.0-SNAPSHOT</version>
</parent>
<artifactId>langchain4j-ai-tools</artifactId>
<properties>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!--导入低阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<!--导入高阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
</dependencies>
</project>

22
langchain4j-ai-tools/src/main/java/com/iwe3/langchain4j/ToolsApplication.java Normal file
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package com.iwe3.langchain4j;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.Bean;
import org.springframework.web.client.RestTemplate;
@SpringBootApplication
public class ToolsApplication {
/**
* 创建RestTemplate的实例
* @return
*/
@Bean
public RestTemplate restTemplate(){
return new RestTemplate();
}
public static void main(String[] args) {
SpringApplication.run(ToolsApplication.class,args);
}
}

20
langchain4j-ai-tools/src/main/java/com/iwe3/langchain4j/config/LLMConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class LLMConfig {
@Bean(name = "qwen")
public ChatModel chatModelQwen(){
/*大模型3件套：apikey ,model-name,base-url */
return OpenAiChatModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.modelName("qwen-plus")
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
}
}

25
langchain4j-ai-tools/src/main/java/com/iwe3/langchain4j/config/SeparateChatAssistantConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.store.memory.chat.InMemoryChatMemoryStore;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class SeparateChatAssistantConfig {
/**
* 聊天记忆提供器
* 配置：采用memoryId来完成隔离
* @return
*/
@Bean
ChatMemoryProvider chatMemoryProvider() {
return memoryId -> MessageWindowChatMemory.builder()
.id(memoryId)
.maxMessages(10).chatMemoryStore(new InMemoryChatMemoryStore())
.build();
//如果未来想自定义 -> 则自己写一个类实现 ChatMemoryStore
}
}

32
langchain4j-ai-tools/src/main/java/com/iwe3/langchain4j/service/ChatAssistant.java Normal file
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package com.iwe3.langchain4j.service;
import dev.langchain4j.service.MemoryId;
import dev.langchain4j.service.UserMessage;
import dev.langchain4j.service.V;
import dev.langchain4j.service.spring.AiService;
import dev.langchain4j.service.spring.AiServiceWiringMode;
/**
* 知识出处:
* https://docs.langchain4j.dev/tutorials/spring-boot-integration/#spring-boot-starter-for-declarative-ai-services
*/
@AiService(wiringMode = AiServiceWiringMode.EXPLICIT
,chatModel = "qwen",
tools = {"weatherTools"},
chatMemoryProvider = "chatMemoryProvider")
public interface ChatAssistant {
/**
* 聊天
* @param memoryId
* @param userMessage
* @return
*/
@UserMessage("请用粤语回答问题，并且添加一些表情符号。 {{msg}}")
String chat(@MemoryId int memoryId, @V("msg") String userMessage);
/**
* 普通聊天
* @param prompt
* @return
*/
String chat(String prompt);
}

27
langchain4j-ai-tools/src/main/java/com/iwe3/langchain4j/tools/WeatherTools.java Normal file
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package com.iwe3.langchain4j.tools;
import dev.langchain4j.agent.tool.P;
import dev.langchain4j.agent.tool.Tool;
import dev.langchain4j.agent.tool.ToolMemoryId;
import jakarta.annotation.Resource;
import org.springframework.stereotype.Component;
import org.springframework.web.client.RestTemplate;
@Component
public class WeatherTools {
@Resource
private RestTemplate restTemplate;
//name 是工具描述 value是工具补充信息
@Tool(name = "查询天气",value = "根据城市编码调用高德开发平台，返回对应城市的天气情况")
public String queryWeather(@ToolMemoryId int memoryId,
@P(value = "城市编码",required = true) String citycode){
System.out.println("查询天气情况---> 开始 ---> memoryId=" + memoryId);
//高德地图：查询天气
//https://lbs.amap.com/api/webservice/guide/api-advanced/weatherinfo
var key = "cf6cb4245c630692d2f13c1cd1ad1632";
var url = "https://restapi.amap.com/v3/weather/weatherInfo?key="+key+"&city="+citycode+"&extensions=all";
return restTemplate.getForObject(url, String.class);
}
}

5
langchain4j-ai-tools/src/main/resources/application.yml Normal file
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server:
port: 9009
spring:
application:
name: langchain4j-ai-tools

26
langchain4j-ai-tools/src/test/java/com/iwe3/langchain4j/ToolsApplicationTest.java Normal file
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package com.iwe3.langchain4j;
import com.iwe3.langchain4j.service.ChatAssistant;
import jakarta.annotation.Resource;
import org.junit.jupiter.api.Test;
import org.springframework.boot.test.context.SpringBootTest;
@SpringBootTest
class ToolsApplicationTest {
@Resource
private ChatAssistant chatAssistant;
@Test
public void getWeatherByMemoryId(){
var res = chatAssistant.chat(1,"成都今天天气如何？城市编码：510100");
System.out.println("res = " + res);
}
@Test
public void getWeather(){
var res = chatAssistant.chat("成都今天天气如何？城市编码：510100");
System.out.println("res = " + res);
}
}

96
langchain4j-ai-xiaoai-agent/pom.xml Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.iwe3</groupId>
<artifactId>langchain4j-ai-java</artifactId>
<version>1.0-SNAPSHOT</version>
</parent>
<artifactId>langchain4j-ai-xiaoai-agent</artifactId>
<properties>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<knife4j.version>4.3.0</knife4j.version>
</properties>
<dependencies>
<!-- 接入阿里云百炼平台 -->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-community-dashscope-spring-boot-starter</artifactId>
</dependency>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-pinecone</artifactId>
</dependency>
<!-- 简单的rag实现 -->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-easy-rag</artifactId>
</dependency>
<!-- 引入德鲁伊数据源连接池的启动器 -->
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>druid-spring-boot-3-starter</artifactId>
<version>1.2.27</version>
</dependency>
<dependency>
<groupId>com.mysql</groupId>
<artifactId>mysql-connector-j</artifactId>
<scope>runtime</scope>
</dependency>
<dependency>
<groupId>org.mybatis.spring.boot</groupId>
<artifactId>mybatis-spring-boot-starter</artifactId>
<version>3.0.5</version>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
</dependency>
<!-- Spring Boot Starter Data MongoDB -->
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-data-mongodb</artifactId>
</dependency>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-reactor</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!--导入低阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<!--导入高阶依赖-->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-spring-boot-starter</artifactId>
<version>1.9.1-beta17</version>
</dependency>
<!-- 前后端分离中的后端接口测试工具 -->
<dependency>
<groupId>com.github.xiaoymin</groupId>
<artifactId>knife4j-openapi3-jakarta-spring-boot-starter</artifactId>
<version>${knife4j.version}</version>
</dependency>
</dependencies>
</project>

13
langchain4j-ai-xiaoai-agent/src/main/java/com/iwe3/langchain4j/XiaoAIAgentApplication.java Normal file
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package com.iwe3.langchain4j;
import org.mybatis.spring.annotation.MapperScan;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@MapperScan("com.iwe3.langchain4j.mapper")
@SpringBootApplication
public class XiaoAIAgentApplication {
public static void main(String[] args) {
SpringApplication.run(XiaoAIAgentApplication.class,args);
}
}

32
langchain4j-ai-xiaoai-agent/src/main/java/com/iwe3/langchain4j/config/EmbeddingStoreConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.pinecone.PineconeEmbeddingStore;
import dev.langchain4j.store.embedding.pinecone.PineconeServerlessIndexConfig;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class EmbeddingStoreConfig {
@Autowired
private EmbeddingModel embeddingModel;
@Bean
public EmbeddingStore<TextSegment> embeddingStore() {
// 创建向量存储
return PineconeEmbeddingStore.builder()
.apiKey(System.getenv("PINECONE_API_KEY"))
.index("xiaoai-index") // 如果指定的索引不存在，将创建一个新的索引
.nameSpace("xiaoai-namespace") // 如果指定的名称空间不存在，将创建一个新的名称空间
.createIndex(PineconeServerlessIndexConfig.builder()
.cloud("AWS") // 指定索引部署在 AWS 云服务上。
.region("us-east-1") // 指定索引所在的 AWS 区域为 us-east-1。
.dimension(embeddingModel.dimension()) // 指定索引的向量维度，该维度与 embeddingModel 生成的向量维度相同。
.build())
.build();
}
}

23
langchain4j-ai-xiaoai-agent/src/main/java/com/iwe3/langchain4j/config/LLMConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.model.chat.StreamingChatModel;
import dev.langchain4j.model.openai.OpenAiStreamingChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class LLMConfig {
/**
* @Description: 流式对话接口 StreamingChatModel
*/
@Bean
public StreamingChatModel streamingChatModel(){
return OpenAiStreamingChatModel.builder()
.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
.modelName("qwen-plus")
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
}
}

65
langchain4j-ai-xiaoai-agent/src/main/java/com/iwe3/langchain4j/config/MongoChatMemoryStore.java Normal file
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package com.iwe3.langchain4j.config;
import com.iwe3.langchain4j.entity.ChatMessages;
import dev.langchain4j.data.message.ChatMessage;
import dev.langchain4j.data.message.ChatMessageDeserializer;
import dev.langchain4j.data.message.ChatMessageSerializer;
import dev.langchain4j.store.memory.chat.ChatMemoryStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.data.mongodb.core.MongoTemplate;
import org.springframework.data.mongodb.core.query.Criteria;
import org.springframework.data.mongodb.core.query.Query;
import org.springframework.data.mongodb.core.query.Update;
import org.springframework.stereotype.Component;
import java.util.LinkedList;
import java.util.List;
@Component
public class MongoChatMemoryStore implements ChatMemoryStore {
@Autowired
private MongoTemplate mongoTemplate;
/**
* 根据聊天ID查询所有消息
* @param memoryId
* @return
*/
@Override
public List<ChatMessage> getMessages(Object memoryId) {
Criteria criteria = Criteria.where("memoryId").is(memoryId);
Query query = new Query(criteria);
ChatMessages chatMessages = mongoTemplate.findOne(query, ChatMessages.class);
if (chatMessages == null) return new LinkedList<>();
return ChatMessageDeserializer.messagesFromJson(chatMessages.getContent());
}
/**
* 根据聊天id修改聊天消息
* @param memoryId
* @param messages
*/
@Override
public void updateMessages(Object memoryId, List<ChatMessage> messages) {
Criteria criteria = Criteria.where("memoryId").is(memoryId);
Query query = new Query(criteria);
Update update = new Update();
update.set("content", ChatMessageSerializer.messagesToJson(messages));
// 根据query条件能查询出文档，则修改文档；否则新增文档
mongoTemplate.upsert(query, update, ChatMessages.class);
}
/**
* 根据聊天ID删除聊天消息
* @param memoryId
*/
@Override
public void deleteMessages(Object memoryId) {
Criteria criteria = Criteria.where("memoryId").is(memoryId);
Query query = new Query(criteria);
mongoTemplate.remove(query, ChatMessages.class);
}
}

27
langchain4j-ai-xiaoai-agent/src/main/java/com/iwe3/langchain4j/config/SeparateChatAssistantConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import jakarta.annotation.Resource;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class SeparateChatAssistantConfig {
@Resource
private MongoChatMemoryStore mongoChatMemoryStore;
/**
* 指定使用-> mongoChatMemoryStore 来存储
* @return
*/
@Bean
ChatMemoryProvider chatMemoryProvider() {
return memoryId -> MessageWindowChatMemory.builder()
.id(memoryId)
.maxMessages(30).
chatMemoryStore(mongoChatMemoryStore)
.build();
}
}

63
langchain4j-ai-xiaoai-agent/src/main/java/com/iwe3/langchain4j/config/XiaoAiAgentConfig.java Normal file
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package com.iwe3.langchain4j.config;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.data.document.parser.TextDocumentParser;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.rag.content.retriever.ContentRetriever;
import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import java.nio.file.FileSystems;
@Configuration
public class XiaoAiAgentConfig {
@Autowired
private EmbeddingStore embeddingStore;
@Autowired
private EmbeddingModel embeddingModel;
@Bean
ContentRetriever contentRetrieverInPinecone() {
// 创建一个 EmbeddingStoreContentRetriever 对象，用于从嵌入存储中检索内容
return EmbeddingStoreContentRetriever
.builder()
// 设置用于生成嵌入向量的嵌入模型
.embeddingModel(embeddingModel)
// 指定要使用的嵌入存储
.embeddingStore(embeddingStore)
// 设置最大检索结果数量，这里表示最多返回 1 条匹配结果
.maxResults(1)
// 设置最小得分阈值，只有得分大于等于 0.8 的结果才会被返回
.minScore(0.8)
// 构建最终的 EmbeddingStoreContentRetriever 实例
.build();
}
/**
* 配置RAG检索增强 文档库
* @return
*/
@Bean
ContentRetriever contentRetrieverInMemory() {
// 使用FileSystemDocumentLoader读取指定目录下的知识库文档
var pathMatcher = FileSystems.getDefault()
.getPathMatcher("glob:*\\.md");
var documents = FileSystemDocumentLoader
.loadDocuments("D:/documents", pathMatcher, new TextDocumentParser());
// 使用内存向量存储
InMemoryEmbeddingStore<TextSegment> db = new InMemoryEmbeddingStore<>();
// 使用默认的文档分割器
EmbeddingStoreIngestor.ingest(documents, db);
// 从嵌入存储（EmbeddingStore）里检索和查询内容相关的信息
return EmbeddingStoreContentRetriever.from(db);
}
}

26
langchain4j-ai-xiaoai-agent/src/main/java/com/iwe3/langchain4j/controller/XiaoAIController.java Normal file
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package com.iwe3.langchain4j.controller;
import com.iwe3.langchain4j.record.ChatRecord;
import com.iwe3.langchain4j.service.XiaoAIChatAssistant;
import io.swagger.v3.oas.annotations.Operation;
import io.swagger.v3.oas.annotations.Parameter;
import io.swagger.v3.oas.annotations.tags.Tag;
import jakarta.annotation.Resource;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;
import reactor.core.publisher.Flux;
@Tag(name="小艾")
@RestController
@RequestMapping("/xiaoai")
public class XiaoAIController {
@Resource
private XiaoAIChatAssistant xiaoAIChatAssistant;
@Operation(summary = "对话")
@PostMapping("/chat")
public Flux<String> chat(@RequestBody ChatRecord record){
return xiaoAIChatAssistant.chat(record.memoryId(),record.message());
}
}

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